Weighted Optimal Markov Model of a Single Outcome: Ipsative Standardization of Ordinal Ratings is Unnecessary

Paul R. Yarnold

Optimal Data Analysis, LLC

This note empirically compares the use of raw vs. ipsatively standardized variables in optimal weighted Markov analysis involving a series for a single outcome—presently, ratings of sleep difficulties for an individual. Findings indicate that the raw score and ipsatively standardized ordinal ratings yield equivalent results in such designs.

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More On: “Optimizing Suboptimal Classification Trees: S-PLUS® Propensity Score Model for Adjusted Comparison of Hospitalized vs. Ambulatory Patients with Community-Acquired Pneumonia”

Paul R. Yarnold

Optimal Data Analysis, LLC

A recent article optimized ESS of a suboptimal classification tree model that discriminated hospitalized vs. ambulatory patients with community acquired pneumonia (CAP). This note suggests possible alternatives for two original attributes as a means of increasing model accuracy: patient disease-specific knowledge vs. “college education”, and patient-specific functional status and social support vs. “living arrangement”.

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Optimizing Suboptimal Classification Trees: S-PLUS® Propensity Score Model for Adjusted Comparison of Hospitalized vs. Ambulatory Patients with Community-Acquired Pneumonia

Paul R. Yarnold

Optimal Data Analysis, LLC

Pruning to maximize model accuracy (requiring simple hand computation) is applied to a classification tree model developed via S-PLUS to create propensity scores to improve causal inference in comparing hospitalized vs. ambulatory patients with community-acquired pneumonia. Research reported herein constitutes a thought-provoking example of a striking misalliance between forward analytic thinking and vestige statistical tools—a condition that dominates the empirical literature today. Modifications of ubiquitous methodological practices are suggested.

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